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cifar100_eval.py
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cifar100_eval.py
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import argparse
import torch
from transformers import BertGenerationTokenizer, BertGenerationDecoder, BertGenerationConfig
import os
from dataloaders.ZO_Clip_loaders import cifar100_single_isolated_class_loader
from clip.simple_tokenizer import SimpleTokenizer as clip_tokenizer
from tqdm import tqdm
import numpy as np
from sklearn.metrics import roc_auc_score
import torchvision
import time
def tokenize_for_clip(batch_sentences, tokenizer):
default_length = 77 # CLIP default
sot_token = tokenizer.encoder['<|startoftext|>']
eot_token = tokenizer.encoder['<|endoftext|>']
tokenized_list = []
for sentence in batch_sentences:
text_tokens = [sot_token] + tokenizer.encode(sentence) + [eot_token]
tokenized = torch.zeros((default_length), dtype=torch.long)
tokenized[:len(text_tokens)] = torch.tensor(text_tokens)
tokenized_list.append(tokenized)
tokenized_list = torch.stack(tokenized_list)
return tokenized_list
def greedysearch_generation_topk(clip_embed):
max_len=77
N = 1 # batch has single sample
max_len=77
target_list = [torch.tensor(berttokenizer.bos_token_id)]
top_k_list = []
bert_model.eval()
for i in range(max_len):
target = torch.LongTensor(target_list).unsqueeze(0)
position_ids = torch.arange(0, len(target)).expand(N, len(target)).to(device)
with torch.no_grad():
out = bert_model(input_ids=target.to(device),
position_ids=position_ids,
attention_mask=torch.ones(len(target)).unsqueeze(0).to(device),
encoder_hidden_states=clip_embed.unsqueeze(1).to(device),
)
pred_idx = out.logits.argmax(2)[:, -1]
_, top_k = torch.topk(out.logits, dim=2, k=35)
top_k_list.append(top_k[:, -1].flatten())
target_list.append(pred_idx)
if len(target_list) == 10: # the entitiy word is in at most first 10 words
break
top_k_list = torch.cat(top_k_list)
return target_list, top_k_list
def image_decoder(clip_model, berttokenizer, device, split, image_loaders=None):
seen_labels = split[:20]
seen_descriptions = [f"This is a photo of a {label}" for label in seen_labels]
targets = torch.tensor(2000*[0] + 8000*[1])
ood_probs_sum = []
max_num_entities = 0
for i, semantic_label in enumerate(split):
loader = image_loaders[semantic_label]
start_time = time.time()
for idx, image in enumerate(tqdm(loader)):
with torch.no_grad():
clip_out = clip_model.encode_image(image.to(device)).float()
clip_extended_embed = clip_out.repeat(1, 2).type(torch.FloatTensor)
#greedy generation
target_list, topk_list = greedysearch_generation_topk(clip_extended_embed)
target_tokens = [berttokenizer.decode(int(pred_idx.cpu().numpy())) for pred_idx in target_list]
topk_tokens = [berttokenizer.decode(int(pred_idx.cpu().numpy())) for pred_idx in topk_list]
unique_entities = list(set(topk_tokens) - set(seen_labels))
if len(unique_entities) > max_num_entities:
max_num_entities = len(unique_entities)
all_desc = seen_descriptions + [f"This is a photo of a {label}" for label in unique_entities]
all_desc_ids = tokenize_for_clip(all_desc, cliptokenizer)
image_feature = clip_model.encode_image(image.cuda()).float()
image_feature /= image_feature.norm(dim=-1, keepdim=True)
text_features = clip_model.encode_text(all_desc_ids.cuda()).float()
text_features /= text_features.norm(dim=-1, keepdim=True)
zeroshot_probs = (100.0 * image_feature @ text_features.T).softmax(dim=-1).squeeze()
#detection score is accumulative sum of probs of generated entities
ood_prob_sum = np.sum(zeroshot_probs[20:].detach().cpu().numpy())
ood_probs_sum.append(ood_prob_sum)
end_time=time.time()
auc_sum = roc_auc_score(np.array(targets), np.squeeze(ood_probs_sum))
print('sum_ood AUROC={}'.format(auc_sum))
return auc_sum
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--trained_path', type=str, default='./trained_models/COCO/')
args = parser.parse_args()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
args.saved_model_path = args.trained_path + '/ViT-B32/'
if not os.path.exists(args.saved_model_path):
os.makedirs(args.saved_model_path)
# initialize tokenizers for clip and bert, these two use different tokenizers
berttokenizer = BertGenerationTokenizer.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder')
clip_model = torch.jit.load(os.path.join('./trained_models', "{}.pt".format('ViT-B-32'))).to(device).eval()
cliptokenizer = clip_tokenizer()
bert_config = BertGenerationConfig.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
bert_config.is_decoder=True
bert_config.add_cross_attention=True
bert_model = BertGenerationDecoder.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder', config=bert_config).to(device).train()
bert_model.load_state_dict(torch.load(args.saved_model_path + 'model.pt')['net'])
cifar100_loaders = cifar100_single_isolated_class_loader()
dset = torchvision.datasets.CIFAR100(root='./data/', train=False, download=True)
idx2class = {v:k for k,v in dset.class_to_idx.items()}
splits = [list(range(20)), list(range(20, 40)), list(range(40, 60)), list(range(60, 80)), list(range(80, 100))]
auc_list = []
idx_splits = []
for seen_split in splits:
split=seen_split+list(set(list(range(100)))-set(seen_split))
label_split = [idx2class[idx] for idx in split]
print(label_split)
auc = image_decoder(clip_model, berttokenizer, device, label_split, image_loaders=cifar100_loaders)
auc_list.append(auc)
print(auc_list)
print('mean AUC={}'.format(np.mean(auc_list)), ' std={}'.format(np.std(auc_list)))